The field of reinforcement learning and graph drawing is rapidly advancing, with a focus on developing more robust and efficient algorithms. Recent research has explored the use of diffusion models to train robust RL policies, as well as the development of new frameworks for offline safe reinforcement learning. Additionally, there has been significant progress in the area of graph drawing, with new algorithms and techniques being proposed for problems such as the one-sided crossing minimization problem. Notably, the use of parallel algorithms and local crossing minimization techniques has shown promising results. Furthermore, researchers have made advancements in constrained Markov decision processes, distributionally robust reinforcement learning, and reinforcement learning with action-triggered observations. Some noteworthy papers in this area include: Adversarial Diffusion for Robust Reinforcement Learning, which introduces a new method for training robust RL policies using diffusion models. Boundary-to-Region Supervision for Offline Safe Reinforcement Learning, which proposes a novel framework for offline safe reinforcement learning that enables asymmetric conditioning through cost signal realignment. Extensions of Robbins-Siegmund Theorem with Applications in Reinforcement Learning, which extends the Robbins-Siegmund theorem to almost supermartingales with non-summable zero-order terms, providing new convergence guarantees for stochastic iterative algorithms.